mcp-executor

What problem does it solve?

Traditional AI agent setups often suffer from "token bloat" when every agent loads extensive tool schemas. This Skill solves that by centralizing complex multi-tool operations within a dedicated executor agent, drastically reducing token usage across your AI colony and simplifying the orchestration of sophisticated workflows.

Core Features & Use Cases

• Token-Efficient Multi-Tool Workflows: Execute complex sequences of Model Context Protocol (MCP) tool calls (e.g., fetch data, transform, validate, store) using TypeScript or Python, without burdening other agents with large tool schemas.
• Parallel & Resilient Execution: Perform multiple independent operations concurrently and implement robust error recovery with retry logic and fallbacks.
• Centralized MCP Expertise: Delegate advanced data processing, file system interactions, and database operations to a specialist agent.
• Use Case: An agent needs to query a database, process the results, write them to a file, and then commit the file to Git. Instead of handling all MCP schemas and logic itself, it sends a single request to the mcp-executor agent, which performs the entire workflow efficiently and cost-effectively.

Quick Start

To request a task from the MCP Executor agent, send it a message detailing the workflow. For example, ask it to "Execute MCP workflow: Fetch user data from database and generate report" specifying the pattern (e.g., multi-tool-workflow), the language (TypeScript or Python), and the MCP tools required, such as mcp__database__query and mcp__filesystem__writeFile. The executor will handle the execution and return the results via message.